The present invention generally relates to image processing. In particular, the present invention relates to signal-to-noise ratio dependent image processing.
Medical diagnostic imaging systems encompass a variety of imaging modalities, such as x-ray systems, computerized tomography (CT) systems, ultrasound systems, electron beam tomography (EBT) systems, magnetic resonance (MR) systems, and the like. Medical diagnostic imaging systems generate images of an object, such as a patient, for example, through exposure to an energy source, such as x-rays passing through a patient, for example. The generated images may be used for many purposes. For instance, internal defects in an object may be detected. Additionally, changes in internal structure or alignment may be determined. Fluid flow within an object may also be represented. Furthermore, the image may show the presence or absence of objects in an object. The information gained from medical diagnostic imaging has applications in many fields, including medicine, manufacturing, and security.
Images are typically formed from pixel (picture element) image data acquired from an imaging system. Acquisition of image data defining each pixel varies depending on the imaging modality used to obtain the data. Discrete pixel images included in an array or matrix of pixels having varying properties, such as intensity and color. Generally, each pixel is represented by a signal. The signal is typically a digitized value representative of a detected parameter, such as an excitation of material in each pixel region producing radiation, for example. To facilitate interpretation of the image, the pixel values must be filtered and processed to enhance definition of features of interest to an observer.
Forming the best possible image at all times for different anatomies and patient types is important to diagnostic imaging systems. Poor image quality may prevent reliable analysis of the image. For example, a decrease in image contrast quality may yield an unreliable image that is not usable clinically. Noise may be introduced in image data due to defects, imperfections, and other interference in the imaging system or the object being imaged. Noise in an image may result in blurring, streaking, or introduction of ghost images or artifacts in a resulting image.
Anatomical details become more obscure with greater noise relative to the data signal. Therefore, a low signal-to-noise ratio (SNR) is undesirable. However, a low SNR often occurs, such as when a short scan duration or lower dose is important. Additionally, in some modalities, such as magnetic resonance imaging, a signal intensity of different tissue types may be suppressed to varying degrees.
In order to address a low SNR in images, an SNR and/or a contrast-to-noise ratio (CNR) is first measured. Various methods are available to measure SNR and/or CNR by attempting to quantify an amount of signal intensity variation or noise that is unrelated to an anatomy being imaged. In one method, for example, two sequential images may be selected to measure SNR. The image data of image one is analyzed to determine the center of the image for positioning a region of interest (ROI). A signal value (S) is computed as a mean pixel value in a ROI covering 80% of a phantom in the first image. Subtracting the second image from the first image creates a difference image. The same ROI is used to calculate a standard deviation (SD) of the subtracted image. Noise (N) is calculated as (SD)/sqrt (2). In another method, where an imaged object does not fill the entire field of view, a signal may be measured in an air region surrounding the object to establish a noise level.
Various techniques have been employed to enhance discrete pixel images to facilitate interpretation of the images. Enhancement or image filtering techniques may employ identification of contrast regions, edges, and other image components, for example. Image components are defined by a series of pixels or groups of pixels within an image matrix. Smoothing and sharpening steps may be employed to enhance certain edges or contrast regions or to de-emphasize specific areas not considered regions of interest. Current techniques, however, may not provide a desired image quality and image filter performance.
Interpolation of image data may produce or change image noise characteristics in an image. Image filters that do not consider variability in interpolation may produce sub-optimal images. Additionally, image filter frameworks that do not consider interpolation variances may require more time and be less efficient in image processing. Inefficiency and delay may impact customers seeking to use the images. Inefficiencies in the image filter framework may also involve more data sets to facilitate image tuning.
Image filtering is generally also applied to computationally improve SNR. However, current image filtering methods do not adequately compute noise in individual images. Therefore, current methods use multiple sets of global parameters to address various levels of noise. Even if a uniform noise level is assumed throughout a given image, the SNR among different regions (corresponding to different tissue types) will vary. Consequently, no single imaging filter with a fixed parameter set when applied to an image may be optimal for all regions within the image. Furthermore, there are no known methods to perform signal dependent noise mitigation and enhancement at every pixel throughout an image.
Therefore, a need exists for an improved image filtering system and method. A system and method that processes an image using signal dependent filtering would be highly desirable.